Please join us for an in person job talk featuring Lauren Childs, Associate Professor of Mathematics at Virginia Tech, https://personal.math.vt.edu//lchilds/ Please contact me tdpence@wisc.edu if you are unable to join in person, but would still like to attend the seminar. We will make a recording available afterward. All faculty, staff, and students are encouraged to come! Please see abstract below.
Title:
Modeling to Aid Malaria Control: Bridging Immunology, Ecology, and Epidemiology
Abstract:
The importance of understanding, predicting, and controlling infectious disease has become increasingly evident during the current COVID-19 pandemic. In particular, the pandemic highlighted the need for interpretable, quantitative models that link mechanism with data while accounting for variability. Despite significant effort and advances, infectious disease dynamics remain incompletely understood, in part due to the lack of heterogeneity considered in immunological, ecological, and epidemiological aspects, which produce complicated, non-linear feedbacks. In this talk, we will focus on the mosquito-transmitted disease malaria, one of the deadliest infectious diseases globally, and discuss two models aimed at furthering control efforts. One model incorporates changes to life-history traits of mosquitoes, brought about by novel intervention strategies in lieu of insecticides. We use recent evidence on compounds that target biological processes key to malaria transmission or directly target the parasite in the mosquito in a discrete time model of mosquito population dynamics and malaria transmission. Incorporating these effects predicts that the inclusion of these compounds on mosquito nets would significantly reduce the burden of malaria across all ranges of prevalence and levels of insecticide resistance. These compounds show great promise in preventing transmission of the malaria parasite without completely abrogating the mosquito population. A second model specifically tracks acquisition and loss of immunity to malaria across a population. Here, we study the role of vaccination and immunity feedback on severe disease and malaria incidence, through a combination of theory and simulation of a partial differential equation model. Using demographic and immunological data, we parameterize our model to simulate realistic scenarios in Kenya. Our work sheds new light on the role of natural- and vaccine-acquired immunity in malaria control.